David B. Lindell

Assistant Professor
Department of Computer Science
University of Toronto

I’m an Assistant Professor in the Department of Computer Science at the University of Toronto and founding member of the Toronto Computational Imaging Group. My work is at the intersection of machine learning, computational imaging, and computer vision. Along these lines I’ve worked on next-generation computational imaging systems for imaging around corners and through scattering media, and new machine learning algorithms for representing and processing signals. My work is relevant to a broad range of applications in computer graphics, vision, and remote sensing.

Students interested in joining my group starting in fall 2023 should apply to the Department of Computer Science. More on this page.

news

Oct 10, 2022

Two papers accepted to NeurIPS 2022–Check out Residual MFNs and Neural Articulated Radiance Fields!

Mar 10, 2022

BACON accepted to CVPR 2022!

Dec 9, 2021

We describe a new type of interpretible neural network with an analytical Fourier spectrum in BACON: Band-Limited Coordinate Networks.

Aug 9, 2021

I’m honored to receive the 2021 SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention Award!

May 7, 2021

Our paper on scaling up implicit representations using adaptive coordinate networks is accepted to SIGGRAPH 2021!

Mar 1, 2021

AutoInt accepted to CVPR 2021! Code is also available here.

Jan 7, 2021

Officially graduated! My dissertation is entitled Computational Imaging with Single-Photon Detectors.

Dec 3, 2020

Our method to solve integral equations with neural networks is out! AutoInt: Automatic integration for fast neural volume rendering.

Sep 1, 2020

Our paper on Sinusoidal Representation Networks (SIREN) was accepted as an oral to NeurIPS (1% acceptance rate).

Sep 1, 2020

My paper on imaging through scattering media was published in Nature Communications and featured in Stanford News.

Aug 1, 2020

My thesis presentation “Computational Single-Photon Imaging” received the honorable mention award at the SIGGRAPH Thesis Fast Forward!

Aug 1, 2020

My course on Computational time-resolved imaging, single-photon sensing, and non-line-of-sight imaging is live at SIGGRAPH! I’m joined by excellent instructors Matthew O’Toole, Ramesh Raskar, and Srinivas Narasimhan.

Jul 1, 2020
Jul 1, 2020

I was recognized as an outstanding reviewer for CVPR 2020! (136/3663 reviewers selected)

Jun 1, 2020
May 1, 2020

I’m co-chairing the 9th annual Computational Cameras and Displays workshop at CVPR 2020 with Achuta Kadambi and Katie Bouman.

Mar 1, 2020

Update: My talk is featured on the TED website with nearly a quarter million views!

Jan 1, 2020

My TedxBeaconStreet talk on “a camera to see around corners” is up on YouTube!

May 1, 2019

Two papers accepted! Acoustic Non-Line-of-Sight Imaging was accepted as an oral to CVPR, and Wave-Based Non-Line-of-Sight Imaging Using Fast f-k Migration was accepted to SIGGRAPH.

Jun 1, 2018

I’m interning at the Intelligent Systems Lab at Intel this summer with Vladlen Koltun.

Mar 1, 2018

Our paper on Seeing around corners was published in Nature!

selected publications

  1. Lindell, D. B., Van Veen, D., Park, J. J., and Wetzstein, G.
    In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2022
  2. Martel, Julien N.P., Lindell, David B., Lin, Connor Z., Chan, Eric R., Monteiro, M., and Wetzstein, G.
    ACM Trans. Graph. (SIGGRAPH) 2021
  3. Lindell, D. B.*, Martel, J. N. P.*, and Wetzstein, G.
    In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021
  4. Sitzmann, V., Martel, J. N. P., Bergman, A. W., Lindell, D. B., and Wetzstein, G.
    In Advances in Neural Information Processing Systems (NeurIPS) 2020
  5. Lindell, D. B., Wetzstein, G., and O’Toole, M.
    ACM Trans. Graph. (SIGGRAPH) 2019
  6. O’Toole, M., Lindell, D. B., and Wetzstein, G.
    Nature 2018